Boost Development Speed with AI Coding Assistant and Cloud Native AI Review: A Step‑by‑Step Guide

Background

Software development complexity continues to increase while product iteration cycles are shortening, putting pressure on developers to deliver high‑quality code quickly and on teams to allocate resources for manual code reviews.

User Core Demands

Efficiency: Reduce repetitive coding and debugging time, focusing on core logic.

Quality Assurance: Avoid human errors and ensure maintainable, standards‑compliant code.

Cost Optimization: Lower rework and defect‑fixing costs to speed up product releases.

Solution Overview

The proposed solution combines an AI coding assistant (e.g., Tongyi Lingma) with Cloud Native AI review (provided by Alibaba Cloud Codeup). The assistant generates code from user requirements, while the AI review automatically checks the code for defects, style violations, and security issues.

Solution Highlights

Intelligent code generation, Q&A, and programming agents.

AI‑driven code review with customizable rules, automatic title/description generation, and conversational feedback.

Support for multiple collaboration modes, automatic CR creation, and branch‑specific review settings.

Workflow Steps

User submits a development or bug‑fix requirement.

Tongyi Lingma generates code based on the requirement (human final review required before production).

Codeup creates a pull‑request (CR) automatically via push‑review mode.

Cloud Native AI review evaluates the code, providing suggestions and defect detection.

Steps 2‑4 repeat until the code meets quality standards.

Code is delivered and deployed through Cloud Native pipelines.

Practical Guide

Initialize a Spring‑Boot demo repository, enable push‑review mode, and clone the repo locally. Then use Tongyi Lingma to implement a new feature: a file‑upload endpoint that stores files in /tmp/files and limits size to 1 MB.

新增一个 FilesController 实现接收文件上传并保存到 /tmp/files 目录下，限制上传的文件不能超过1MB

Run the service, upload a file smaller than 1 MB to verify success, and test uploading a file larger than 1 MB to observe an HTTP 413 response.

Code Review and Validation

After committing and pushing the changes, Codeup automatically creates a CR. The AI reviewer flags any critical issues. To demonstrate detection, intentionally modify the constant MAX_FILE_SIZE to 1024 MB and invert the size‑check logic; the AI review then reports the introduced defects.

➜  demo git:(main) ✗ git add .
➜  demo git:(main) ✗ git commit -m "feat: support file upload with file size limit 1MB"
[main 00b96a4] feat: support file upload with file size limit 1MB
 2 files changed, 42 insertions(+)
 create mode 100644 src/main/java/com/example/demo/FilesController.java
➜  demo git:(main) git push
... (remote messages showing CR creation) ...

The CR page shows the AI assistant performing the review, highlighting the reversed size‑check and the incorrect constant value.